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Friday, 15 September 2017

The Naked Scientists are based at Cambridge University's Institute of Continuing Education (ICE). In their own words, they are a team of scientists, doctors and communicators whose passion is to help the general public to understand and engage with the worlds of science, technology and medicine.

They recently contacted me to provide an answer to the following question:

"Water is solid below 0 degrees C, a liquid from 0 to 100 degrees C, and a gas above 100 degrees C. Then why does washing and the roads dry when the temperature isn't 100 degrees C?"
I think the answer's actually quite subtle, for such a deceptively simple-sounding question. My recorded answer can be downloaded from here. Full transcript below!

Interviewer: I asked Thomas Ouldridge from Imperial College London to hang Norm’s question out to dry

Tom: It is true that pure waterwill be a gas (called water vapour) only above 100 degrees Celsius, but temperature isn’t the only factor at play here.

The surrounding pressure also impacts when a substance like watercan be a gas – the higher the pressure, the higher the temperature required to be a gas. You've probably all heard of LPG - this stands for liquified petroleum gas. The chemicals in LPG would normally be a gas, but by keeping LPG in high-pressure cannisters it can be storedas a liquid.

So gases, like [insert humorous reference here], often can't handle pressure. But why is this?

To exist as a gas, water molecules have to be widely spaced out. High enough pressure will simply squeeze them back into their more compact liquid form.

The more you heat the water, the more energy you give to the molecules and the harder they push back against their surroundings. Above 100 degrees Celsius, but not below, water molecules can push back hard enough against the pressure of the atmosphere for pure water to stay as a gas.

Well, personally I tend to hang my washing inside the earth’s atmosphere, and below boiling point, and it still dries. So what’s going on?

Well, let’s consider a puddle, for example. You might think it should stay as aliquid because the temperature is below 100 degrees, and the atmosphere is pushing down on it. However, we don't only have water molecules involved - the system isn't pure. The air above the surface of the puddle contains many other molecules, such as nitrogen and oxygen.

These extra molecules can actually help to push back against the surrounding atmosphere, effectively lowering the pressure that must be supported by the water molecules if they form a gas. It’s like many people helping to lift a weight rather than just one.

In fact, there’s so much more nitrogen and oxygen that they take almost all of the burden of the atmospheric pressure.

This is important: any water molecules that have enough energy to escape from the puddle don’t face the full might of the atmospheric pressure – so they don’t immediately liquidise.

This is why some water vapour can survive in the atmosphere, thanks to the hard work of the other gasses, and we can explain why evaporation happens and puddles (or clothes) dry under normal conditions.

Only a certain amount of water vapour can be supported by the other gasses though, which is why things don’t evaporate immediately, and why movement of air is important if you want things to dry faster.

If you want to see this in action for yourself, lick your wrist and blow on it. It dries almost immediately compared to if you don’t blow. So that’s evaporation covered, but how is that different from boiling?

When water reaches 100 degrees at atmospheric pressure, it has so much energy that the vapour no longer needs the help of other gases to be stable. In our analogy, the water vapour is now like a weightlifter that is strong enough to support the “weight” of the atmosphere on its own.

At this point, bubbles of vapour can form within the liquid itself, converting liquid to gas much faster than slow evaporation from the liquid surface.

Thursday, 6 July 2017

Cells need to sense their environment in order to survive.
For example, some cells measure the concentration of food or the presence of
signalling molecules. We are interested in studying the physical limits to
sensing with limited resources, to understand the challenges faced by cells and
to design synthetic sensors.

We have recently published a paper (arxiv version) where we explore
the interpretation of a metric called the learning rate that has been used to
measure the quality of a sensor (New J. Phys. 16 103024,Phys. Rev E 93 022116). Our motivation
is that in this field a number of metrics (a metric is a number you can
calculate from the properties of the sensor that, ideally, tells you how good
the sensor is) have been applied to make some statement about the quality of
sensing, or limits to sensory performance. For example, a limit of particular interest is
the energy required for sensing. However, it is not always clear how to
interpret these metrics. We want to find out what the learning rate means. If
one sensor has a higher learning rate than another what does that tell you?

The learning rate is defined as the rate at which changes in
the sensor increase the information the sensor has about the signal. The
information the sensor has about the signal is how much your uncertainty about
the state of the signal is reduced by knowing the state of the sensor (this is
known as the mutual information). From this definition, it seems plausible that
the learning rate could be a measure of sensing quality, but it is not clear.
Our approach is a test to destruction – challenge the learning rate in a
variety of circumstances, and try to understand how it behaves and why

To do this we need a framework to model a general signal and
sensor system. The signal hops between discrete states and the sensor also hops
between discrete states in a way that follows the signal. A simple example is a
cell using a surface receptor to detect the concentration of a molecule in its
environment.

The figure shows such a system. The circles represent the
states and the arrows represent transitions between the states. The signal is
the concentration of a molecule in the cell’s environment. It can be in two
states; high or low, where high is double the concentration of low. The sensor
is a single cell surface receptor, which can be either unbound or bound
to a molecule. Therefore, the joint system can be in four different states. The
concentration jumps between its states with rates that don’t depend on the
state of the sensor. The receptor becomes unbound at a constant rate and is
bound at a rate proportional to the molecule concentration.

We calculated the learning rate for several systems,
including the one above, and compared it to the mutual information between the
signal and the sensor. We found that in the simplest case, shown in the figure,
the learning rate essentially reports the correlation between the sensor and
the signal and so it is showing you the same thing as the mutual information.
In more complicated systems the learning rate and mutual information show
qualitatively different behaviour. This is because the learning rate actually
reflects the rate at which the sensor must change in response to the signal,
which is not, in general, the equivalent to the strength of correlations
between the signal and sensor. Therefore, we do not think that the learning
rate is useful as a general metric for the quality of a sensor.

Tuesday, 4 July 2017

Due to the unpredictability of motion at the microscopic scale, molecular
processes have randomness associated with them, exhibiting what we call thermodynamic
fluctuations. A group in Germany lead by Barato and Seifert have written a series of papers,
beginning with "Thermodynamic uncertainty relation for biomolecular processes" (preprint here), exploring how uncertainty in the number of reaction
steps taken by a molecular process is related to the degree to which the
system is constantly consuming energy.

To be more precise, Barato and Seifert consider the number of times a system
completes a cycle in a given time window. A good example of this kind
of setup is the rotary motor F0F1-ATPsynthase (below, image taken from Wikipedia).

This motor is used to create the chemical fuel source of the cell (ATP) from its components (ADP and inorganic phosphate P). In order to drive this process, a current of hydrogen ions flows through the top half of the motor, causing it to systematically rotate in one direction with respect to the bottom half. This rotation is physically linked to the reaction ADP + P -> ATP, and so ATP is created. This one-directional rotational motion only arises because the current of hydrogen ions continuously supplies more energy (more technically, free energy) to the system than is needed to create the ATP. We say that the current of ions drives the system.

In general, small driven systems have a bias towards stepping forward, but there
is still a non-zero probability of stepping backwards due to thermodynamic fluctuations.
We also cannot predict exactly how long the system will take to complete each step of the cycle, and so the time taken per step is variable. Thus the number of cycles completed
in a given time is uncertain. It is, however, possible to define an average
of the net number of cycles in a time window µ and a variance σ2, which is a
mathematical measure of the typical deviation from the average due to fluctuations.
The Fano factor F = σ2/µ gives a measure of the relative importance of
the random fluctuations about the average.

In the paper "Thermodynamic uncertainty relation for biomolecular processes", Barato and Seifert relate the energy consumption and the Fano
factor via F ≤ 2kT /E. Here E is the energy consumed per cycle, T is the
temperature and k is Boltzmann’s constant. This expression means that the
Fano factor is at least as big as the quantity 2kT /E. Thus a cycle which uses
a certain amount of fuel E has an upper limit to its precision, and there is an
evident trade-off between the amount of energy dissipated per cycle and the Fano
factor.

In the original paper, the authors only prove their relation for very simple
processes. However, it has since been generalised in this paper (preprint here). The result is actually based on very
deep statements about the types of fluctuating processes that are possible in
physical systems. One of the challenges now is to take this fundamental insight and apply it to gain a better understanding of practical systems. Fortunately, the F0F1-ATPsynthase rotary motor is not the only example of an interesting biological system that undergoes
driven cycles; the cell contains a huge variety of molecular motors that can also be understood in this way (preprint here). Molecular timekeepers that are vital to the cellular life cycle also depend on driven cycles. Understanding the trade-offs between unwanted variability and energy consumption will be vital in engineering such systems.

Tuesday, 11 April 2017

Single cells, which are essentially bags of chemicals, can achieve remarkable feats of information processing. Humans have designed computers to perform similar tasks in our everyday world. The question of whether it is possible to emulate cells and use molecular systems to perform complex computational tasks in parallel, at an extremely small scale and consuming a low amount of power, is one that has intrigued many scientists.In collaboration with the tenWolde group from AMOLF Amsterdam, we have just published two articles in Physical Review X and Physical Review Letters that get to the heart of this question.

The readout molecules (orange) act as copies of the binding
state of the receptors (purple), through catalytic
phosphorylation/dephosphorylation reactions.

In the first, “The Thermodynamics of computational copying in biochemical systems”, we show that a simple molecular process occurring inside living cells - a phosphorylation/dephosphorylation cycle - is able to copy the state of one protein (for example, whether a food molecule is bound to it or not) into the chemical modification state of another protein (phosphorylated or not). This copy process can be rigorously related to those performed by conventional computers.

We thus demonstrated that living cells can perform the basic computational operation of copying a single bit of information. Moreover, our analysis revealed that these biochemical computations can occur rapidly and at a low power consumption. The article shows precisely how natural systems relate to and differ from traditional computing architectures, and provides a blueprint for building naturally-inspiredsynthetic copying systems that approach the lower limits of power consumption.

The production of a persistent copy from a template.
The separation in the final state is essential.

A more complex natural copy operation is the production of polymer copies from polymer templates, as discussed in this previous post. Such processes are necessary for DNA replication, and also for the production of proteins from DNA templates via intermediate RNA molecules. For cells to function, the data in the original DNA sequence of bases must be faithfully reproduced - each copy therefore involves copying many bits of data. In the second article, "Fundamental costs in the production and destruction of persistent polymer copies", we consider such processes. We point out that these polymer copies must be persistent to be functional. In other words, the end result is two physically separate polymers: it would be useless to produce proteins that couldn't detach from their nucleic acid templates. As a result, the underlying principles are very different from the superficially similar process of self-assembly, in which molecules aggregate together according to specific interactions to form a well-defined structure. In particular, we show that the need to produce persistent copies implies that more accurate copies necessarily have a higher minimal production cost (in terms of resources consumed) than sloppier copies. This result, which is not true if the copies do not need to physically separate from their templates, sets a bound on the function of minimal self-replicating systems.

Additionally, the results suggest that polymer copying processes that occur without external intervention (autonomously) must occur far from equilibrium. Being far from equilibrium means that processes are highly irreversible - taking a forwards step is much more likely than taking a backwards step. This finding draws a sharp distinction with self-assembling systems, that typically assemble most accurately when close to equilibrium. This difference may explain why recent years have shown an enormous growth in the successful design of self-assembling molecular systems, but autonomous synthetic systems that produce persistent copies through chemical means have yet to be constructed.

Taken together, these papers set a theoretical background on which to base the design of synthetic molecular systems that achieve computational processes such as copying and information transmission. The next challenge is now to develop experimental systems that exploit these ideas.

Monday, 3 April 2017

Today I meet with a group of school students (aged 16-18) from the City of London School, who will be working on a project for iGEM this year. iGEM is an international competition for school, undergrad and postgrad teams to design, model and build complex systems by engineering cells. Last year, Imperial won the overall prize, as discussed in this post by Ismael.

Without giving too much away, the students will be working on a system based on a newly-developed molecular device, the toehold switch. Toehold switches are RNA molecules that contain the information required to produce proteins. This information is hidden via interactions within the RNA, which cause it to fold up into a shape that prevents the sequence from being accessed. If, however, a second strand of RNA with the right sequence is present, the structure can be opened up and protein production is possible.

This idea has been around for a reasonable while, but toehold switches are particularly useful, because they provide a better decoupling of the input, output and internal operation of the switch than previous designs. This is the principal of modularity that underlies the work of many of my colleagues here at Imperial, and allows for systematic engineering of molecular systems. This modularity is key to the proposed project.

I've been giving the students advice on how to model the operation of a toehold switch, in order that they can explore the design space before getting into the lab.

Wednesday, 11 January 2017

Biological systems at many scales exploit information to extract energy from their environment. In chemotaxis, single-celled organisms use the location of food molecules to navigate their way to more food; humans use the fact that food is typically found in the cafeteria. Although the general idea is clear, the fundamental physical connection between information and energy is not yet well-understood. In particular, whilst energy is inherently physical, information appears to be an abstract concept, and relating the two consistently is challenging. To overcome this problem, we have designed two microscopic machines that can be assembled out of naturally-occurring biological molecules and exploit information in the environment to charge a chemical battery. The work has just been published as an Editor's selection in Physical Review Letters: http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.118.028101

The basic idea behind the machines is simple, and makes use of pre-existing biology. We employ an enzyme that can take a small phosphate group (one phosphorus and several oxygen atoms bound together) from one molecule and attach it to another – a process known as phosphorylation. Phosphorylation is the principal signaling mechanism within a cell, as enzymes called kinases use phosphyrlation to activate other proteins. In addition to signalling, phosphates are one of the cell’s main stores of energy; chains of phosphate bonds in ATP (the cell’s fuel molecule) act as batteries. By ‘recharging’ ATP through phosphorylation, we store energy in a useful format; this is effectively what mitochondria do via a long series of biochemical reactions.

Fig 1.: The ATP molecule (top) and ADP molecule (bottom). Adenosine (the "A") is the group of atoms on the right of the pictures; the phosphates (the P) are the basic units that form the chains on the left. In ADP (Adenosinediphosphate) there are two phosphates in the chain; in ATP((Adenosinetriphosphate) there are three.

The machines we consider have three main components: the enzyme, the ‘food’ molecule that acts as a source of phosphates to charge ATP, and an activator for the enzyme, all of which are sitting in a solution of ATP and its dephosphorylated form ADP. Food molecules can either be charged (i.e. have a phosphate attached) or uncharged (without phosphate). When the enzyme is bound to an activator, it allows transfer of a phosphate from a charged food molecule to an ADP, resulting in an uncharged food molecule and ATP. The reverse reaction is also possible.

In order to systematically store energy in ATP, we want to activate the enzyme when a charged food molecule is nearby. This is possible if we have an excess of charged food molecules, or if charged food molecules are usually located near activators. In the second case, we're making use of information: the presence of an activator is informative about the possible presence of a charged food molecule. This is a very simple analogue of the way that cells and humans use information as outlined above. Indeed, mathematically, the 'mutual information' between the food and activator molecules is simply how well the presence of an activator indicates the presence of a charged food molecule. This mutual information acts as an additional power supply that we can use to charge our ATP-batteries. We analyse the behaviour of our machines in environments containing information, and find that they can indeed exploit this information, or expend chemical energy in order to generate more information. By using well-known and simple components in our device, we are able to demystify much of the confusion over the connection between abstract information and physical energy.

A nice feature of our designs is that they are completely free-running, or autonomous. Like living systems, they can operate without any external manipulation, happily converting between chemical energy and information on its own. There’s still a lot to do on this subject; we have only analysed the simplest kind of information structure possible and have yet to look at more complex spatial or temporal correlations. In addition, our system doesn’t learn, but relies on ‘hard-coded’ knowledge about the relation between food and activators. It would be very interesting to see how machines that can learn and harness more complex correlation structures would behave.Authored by Tom McGrath